Mutations in the epigenetic regulator ASXL1 are common in myeloid malignancies and portend a near-universally poor prognosis. While multiple mechanisms for mutant ASXL1-dependent oncogenesis have been proposed, none have been functionally validated in the context of the human hematopoietic stem cell, where these mutations almost certainly arise. Here, we extensively characterized a CRISPR-engineered human hematopoietic stem and progenitor cell model of ASXL1 mutations. In this context, mutant ASXL1 expression decreases differentiation, increases clonogenicity in serial replating experiments, and improves engraftment in immunodeficient mice. We also show that endogenous truncating ASXL1 mutations stabilize the protein and confirm that mutant ASXL1 resists proteasomal degradation. At the transcriptional level, these phenotypes are driven by significant repression of immediate early genes and subtle global transcriptional upregulation, especially of genes repressed during normal differentiation. Using protein-interaction screens, genomic and functional approaches, we link the phenotypic changes in ASXL1-mutant cells to increased chromatin binding of RNAPII, BRD4, and the transcription factor MECOM. The association between ASXL1 and MECOM may reflect a direct or indirect interaction. We also observe aberrant RNA polymerase II pausing dynamics, especially at immediate early genes, in ASXL1-mutated cells. Finally, we demonstrate that ASXL1-mutant AML exhibits increased MECOM activity, consistent with our gene-editing models. Collectively, these studies highlight a highly reproducible model of mutant ASXL1 in the appropriate cell context. Further, they are the first to functionally describe the mutant ASXL1 interactome in the context of human HSCs, identifying MECOM and BRD4 as actionable dependencies with therapeutic potential for ASXL1-mutant myeloid malignancies.
Some claim that especially in the field of agile software development the research lags years behind of the practice. In this paper, we characterize the status and main challenges for research on agile software development, and propose a preliminary roadmap, focusing on providing more empirical research, primarily on e...
Torgeir Dingsøyr, T. Dybå, P. Abrahamsson· Agile Conference· 92 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoptio...
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.